title: Neural network-based PET image reconstruction.
abstract: In PET image analysis, conventional deconvolution alone will not give sufficient information for a precise study of a localized brain function. In the deconvolution process, which is a type of inverse problem, it is important to confine the solution space by incorporating a priori knowledge such as the tissue distribution given by MR images as well as smoothness in the blood flow distribution profile. An MR-embedded neural-network model is described to reduce the partial volume effect in the restoration of blood flow profiles from PET images.
authors.affiliation: Tokyo Institute of Technology, Yokohama, Japan. kosugi@pms.titech.ac.jp
authors.forename: Y
authors.initials: Y
authors.lastname: Kosugi
authors.forename: M
authors.initials: M
authors.lastname: Sase
authors.forename: Y
authors.initials: Y
authors.lastname: Suganami
authors.forename: N
authors.initials: N
authors.lastname: Uemoto
authors.forename: T
authors.initials: T
authors.lastname: Momose
authors.forename: J
authors.initials: J
authors.lastname: Nishikawa
mesh_terms: D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D014055:Tomography, Emission-Computed